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TRIPODS+X:RES:Collaborative Research: Learning with Expert-In-The-Loop for Multimodal Weakly Labeled Data and an Application to Massive Scale Medical Imaging

TRIPODS X:RES:协作研究：与专家在环学习多模态弱标记数据及其在大规模医学成像中的应用

基本信息

批准号：
1839332
负责人：
Kayhan batmanghelich
金额：
$ 30万
依托单位：
University of Pittsburgh
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839332&HistoricalAwards=false
关键词：
TRIPODS+X RES Collaborative Research Learning

项目摘要

The methods developed in this project address pressing problems at the interface of machine learning and a life-sciences application. This work hopes to spur a rich variety of followup research that not only builds on the mathematical tools developed but also on the insights gained from the project's focus on interpretable and interactive machine learning. As a key application, implementation of the technology obtained from this research on medical images will enable automatic report generation. This technology could reduce the workload of radiologists significantly, and hence reduce human error and optimize resource utilization. Ultimately, reduction in human error or missing cases in radiology can greatly benefit patient well-being and care. More broadly, the work will enhance the ongoing adoption of data-driven thinking in healthcare and thereby help accelerate new discoveries. This project also impacts education, and involves intellectual and professional development of students at a variety of academic stages. Most applications of machine learning to medical imaging (and other human-centric tasks) focus on supervised learning, which demands a large amount of expensive labeled-data. This limitation recurs throughout applications, while real-world use of machine learning demands robustness as well as an ability to work with limited supervision. This project focuses on developing new machine learning tools for working with weak-supervision, while advancing state-of-the-art in interpretable and interactive learning. Moreover, attention to large-scale data and incorporation of domain knowledge is paid, whenever feasible. The project shall also apply the theoretical advances that it will make to a real-world medical imaging application. Theoretical advances of the proposed work will rely on tools from geometry, especially metric learning (including over infinite dimensional spaces), mathematical models motivated by optimal transport theory, as well as nonlinear representations based on neural networks as well as kernel methods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目中开发的方法解决了机器学习和生命科学应用界面上的紧迫问题。这项工作希望能够激发各种各样的后续研究，这些研究不仅建立在所开发的数学工具的基础上，而且还建立在该项目对可解释和交互式机器学习的关注所获得的见解上。作为一个关键的应用程序，从这项研究中获得的技术在医学图像上的实施将使自动报告生成。这项技术可以大大减少放射科医生的工作量，从而减少人为错误并优化资源利用。最终，减少放射学中的人为错误或遗漏病例可以极大地有益于患者的健康和护理。更广泛地说，这项工作将促进数据驱动思维在医疗保健领域的持续采用，从而有助于加速新的发现。该项目还影响教育，涉及学生在各个学术阶段的智力和专业发展。机器学习在医学成像（和其他以人为中心的任务）中的大多数应用都集中在监督学习上，这需要大量昂贵的标记数据。这种限制在整个应用程序中反复出现，而机器学习的实际使用需要鲁棒性以及在有限监督下工作的能力。该项目的重点是开发新的机器学习工具，用于弱监督，同时推进可解释和交互式学习的最新技术。此外，只要可行，就注意大规模数据和纳入领域知识。该项目还应将其理论进展应用于现实世界的医学成像应用。所提出的工作的理论进展将依赖于几何工具，特别是度量学习（包括无限维空间），由最优传输理论驱动的数学模型，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

项目成果

期刊论文数量（24）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Weakly Supervised Disentanglement by Pairwise Similarities

DOI：
10.1609/aaai.v34i04.5754
发表时间：
2019-06
期刊：
Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence
影响因子：
0
作者：
Junxiang Chen;K. Batmanghelich
通讯作者：
Junxiang Chen;K. Batmanghelich

Label-Noise Robust Domain Adaptation

DOI：
发表时间：
2020-07
期刊：
Proceedings of machine learning research
影响因子：
0
作者：
Xiyu Yu;Tongliang Liu;Mingming Gong;Kun Zhang;K. Batmanghelich;D. Tao
通讯作者：
Xiyu Yu;Tongliang Liu;Mingming Gong;Kun Zhang;K. Batmanghelich;D. Tao

Context Matters: Graph-based Self-supervised Representation Learning for Medical Images

DOI：
10.1609/aaai.v35i6.16620
发表时间：
2020-12
期刊：
Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence
影响因子：
0
作者：
Li Sun;Ke Yu;K. Batmanghelich
通讯作者：
Li Sun;Ke Yu;K. Batmanghelich

Generative-Discriminative Complementary Learning

DOI：
10.1609/aaai.v34i04.6126
发表时间：
2019-04
期刊：
Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence
影响因子：
0
作者：
Yanwu Xu;Mingming Gong;Junxiang Chen;Tongliang Liu;Kun Zhang;K. Batmanghelich
通讯作者：
Yanwu Xu;Mingming Gong;Junxiang Chen;Tongliang Liu;Kun Zhang;K. Batmanghelich

Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays

胸部 X 射线解剖引导弱监督异常定位